Nowadays, machine learning is a favorite tool for data analysis and has solved some conundrums. Thus, many predictors utilized machine learning as algorithms for SAVs had been developed. Initially, most tools got on with the protein stability and functional changes caused by missense variations (Radusky et al., 2018; Schaefer & Rost, 2012; Sim et al., 2012; Vaser, Adusumalli, Leng, Sikic, & Ng, 2016). Further, some predictors are to distinguish benign and pathogenic variations (Sundaram et al., 2018; Yates, Filippis, Kelley, & Sternberg, 2014). Hitherto, some web software demonstrated the connection between SAVs and diseases; however, those are biased towards genetic diseases (I. Adzhubei, Jordan, & Sunyaev, 2013; I. A. Adzhubei et al., 2010; Ferrer-Costa et al., 2005; Lopez-Ferrando, Gazzo, de la Cruz, Orozco, & Gelpi, 2017; Reeb, Hecht, Mahlich, Bromberg, & Rost, 2016). Few predictors are established for cancer, but most of them focus on the specific purpose or particular cancer (B. Wang et al., 2018). Some tools are designed for classifying driver and passenger mutation (Carter et al., 2009; Kaminker, Zhang, Watanabe, & Zhang, 2007; Shihab, Gough, Cooper, Day, & Gaunt, 2013). Though they are useful, a comprehensive predictor in cancer biology research is in pressing demand. In this work, we developed a prediction model that recognizes whether the SAV is cancer-related or neutral. Though numerous predictors have been developed, the critical question is how to build the prediction models and what descriptors of SAV are used (Care, Needham, Bulpitt, & Westhead, 2007). Not only to figure out for each SAV change physically in protein function and structure but also to estimate how it simultaneously contributes to cancer progression. To take into account every kind of SAVs might be a vital feature for cancer, we perform an integrated system to discriminate the cancer-related residues in sequence from multiple predicting models utilizing spread information extracted from the fundamental of protein in this work. We would provide a novel way for cancer research, not only for the clinical outcome but also for prognostic biomarker, and a breakthrough for precision medicine. Besides setting up this predictor for cancer-related variations, it would be helpful to figure out the relationship between SAV and cancer and the underlying mechanisms.